In this work, we investigate the problem of multi-user linearly separable function
computation, where N servers help compute the desired functions (jobs) of K users. In this …

Plagued by slow or failing workers (also known as stragglers), the speedup gain assumed
by distributed learning often falls short. Although substantial efforts have been devoted to …

In this paper, we investigate the problem of multi-user linearly decomposable function
computation, where N servers help compute functions for K users, and where each such …

In distributed computing systems, to mitigate the adverse effect of stragglers on the
computation time, computation redundancy is used. The redundancy can be added …

In distributed matrix multiplication, stragglers present a significant challenge. Coding
techniques are often employed to mitigate this issue; however, their effectiveness is typically …

Many scientific and numeric computations rely on matrix-matrix multiplication as a
fundamental component of their algorithms. It constitutes the building block in many matrix …

We consider distributed learning in the presence of slow and unresponsive worker nodes,
referred to as stragglers. In order to mitigate the effect of stragglers, gradient coding …